Wheat stripe rust is one of the most important and devastating diseases in wheat production. In order to detect wheat stripe rust at an early stage, a visual detection method based on hyperspectral imaging is proposed in this paper. Hyperspectral images of wheat leaves infected by stripe rust for 15 consecutive days were collected, and their corresponding chlorophyll content (SPAD value) were measured using a handheld SPAD-502 chlorophyll meter. The spectral reflectance of the samples were then extracted from the hyperspectral images, using image segmentation based on a leaf mask. The effective wavebands were selected by the loadings of principal component analysis (PCA-loadings) and the successive projections algorithm (SPA). Next, the regression model of the SPAD values in wheat leaves was established, based on the back propagation neural network (BPNN), using the full spectra and the selected effective wavelengths as inputs, respectively. The results showed that the PCA-loadings–BPNN model had the best performance, which modeling accuracy (R_C²) and validation accuracy (R_P²) were 0.921 and 0.918, respectively, and the RPD was 3.363. The number of effective wavelengths extracted by this model accounted for only 3.12% of the total number of wavelengths, thus simplifying the models and improving the rate of operation greatly. Finally, the optimal models were used to estimate the SPAD of each pixel within the wheat leaf images, to generate spatial distribution maps of chlorophyll content. The visualized distribution map showed that wheat leaves infected by stripe rust could be identified six days after inoculation, and at least three days before the appearance of visible symptoms, which provides a new method for the early detection of wheat stripe rust.

Fusarium head blight (FHB), a globally significant agricultural disaster, causes annual losses of dozens of millions of tons of wheat toxins produced by FHB, such as deoxyroscyliaceol, further pose serious threats to human and livestock health. Consequently, rapid and non-destructive determination of FHB severity is crucial for implementing timely and precise scientific control measures, thereby ensuring wheat supply security. Therefore, this study adopts hyperspectral imaging (HSI) combined with a lightweight deep learning model. Firstly, the wheat ears were inoculated with Fusarium fungi at the spike’s midpoint, and HSI data were acquired, yielding 1660 samples representing varying disease severities. Through the integration of multiplicative scatter correction (MSC) and uninformative variable elimination (UVE) methods, features are extracted from spectral data in a manner that optimizes the reduction of feature dimensionality while preserving elevated classification accuracy. Finally, a lightweight FHB severity discrimination model based on MobileNetV2 was developed and deployed as an easy-to-use analysis system. Analysis revealed that UVE-selected characteristic bands for FHB severity predominantly fell within 590–680 nm (chlorophyll degradation related), 930–1043 nm (water stress related) and 738 nm (cell wall polysaccharide decomposition related). This distribution aligns with the synergistic effect of rapid chlorophyll degradation and structural damage accompanying disease progression. The resulting MobileNetV2 model achieved a mean average precision (mAP) of 99.93% on the training set and 98.26% on the independent test set. Crucially, it maintains an 8.50 MB parameter size, it processes data 2.36 times faster, significantly enhancing its suitability for field-deployed equipment by optimally balancing accuracy and operational efficiency. This advancement empowers agricultural workers to implement timely control measures, dramatically improving precision alongside optimized field deployment.

Stripe rust is one of the most common diseases challenging the safe production of wheat. Rapid identification and analysis of urediospores, responsible for disease transmission, are the key to preventing and controlling stripe rust. The current spore detection is time-consuming and requires auxiliary equipment, but many optical detection methods and sensors with high efficiency, sensitivity, and detection ability have been developed and widely used. Thus, the investigation of optical parameters is the prerequisite for optical sensitive detection and is detailed in this study. Firstly, the microscopic images and surface elements of wheat stripe rust urediospores were obtained using a high-resolution electron microscope and an energy spectrum analyzer. The reflectivity and absorptivity spectra in the near-infrared band of active/inactivate urediospores were measured. Further, the optical parameters, such as reflection phase shift, complex refractive index, and dielectric constant, of the urediospores were analyzed based on electromagnetic theory and Kramers–Kronig relation. The results revealed that the urediospores have a strong tolerance to near-infrared light, and the real part of the complex refractive index n was between 1.0 and 1.4. These observations indicate that whether the spores are active or not has little effect on the real part and dielectric constant of the complex refractive index but has a significant impact on the imaginary part and extinction coefficient of the complex refractive index, and the corresponding relationship between the optical properties of urediospores and biological components was established. These findings were verified with HPLC-MS and Near-Infrared Spectroscopy Analysis Technology and lay a theoretical foundation for detecting urediospores of wheat stripe rust by using optical sensors. The study provides a reference for the analysis of optical characteristics of other biological samples.